Jude Alexander asked . 2022-06-07

Could you please help me in Artificial neural network - supervised learning?

%load data
inputs_bn, targets_bn;

%Normalize  - Do i have to normalize the data?
[inputs,ps] = mapminmax(inputs_bn);
[targets,ts] = mapminmax(targets_bn);
HL=[30 50 30];

%inputs
%targets
% Create a Fitting Network
hiddenLayerSize = HL;
net=fitnet(hiddenLayerSize,'traingdx'); % Is this used for predictions?

% Choose Input and Output Pre/Post-Processing Functions
% For a list of all processing functions type: help nnprocess
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};

% Setup Division of Data for Training, Validation, Testing
% For a list of all data division functions type: help nndivide
net.divideFcn = 'dividerand';  % Divide data randomly
net.divideMode = 'sample';  % Divide up every sample

net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;

net.trainFcn = 'trainlm';  % Levenberg-Marquardt
net.trainParam.min_grad=1e-8;

% Choose a Performance Function
%change from
%net.performFcn = 'mse';  % Mean squared error
%change from

%change to
net.performFcn='msereg';
net.performParam.ratio=0.5;
%change to
% Choose Plot Functions
% For a list of all plot functions type: help nnplot
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
  'plotregression', 'plotfit'};

% Train the Network
[net,tr] = train(net,inputs,targets,'useParallel','yes','showResources','yes'); %trainr gave bad results

% Test the Network
outputs11 = net(inputs);
outputs=mapminmax('reverse',outputs11,ts);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs)

% Recalculate Training, Validation and Test Performance
trainTargets = targets .* tr.trainMask{1};
valTargets = targets  .* tr.valMask{1};
testTargets = targets  .* tr.testMask{1};
trainPerformance = perform(net,trainTargets,outputs)
valPerformance = perform(net,valTargets,outputs)
testPerformance = perform(net,testTargets,outputs)

% View the Network
%view(net)

% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure(1), plotfit(net,inputs,targets)
%figure, plotregression(targets,outputs)
figure(111), ploterrhist(errors)

%%%%%%%%

%%%%Load Test DATA
% Target_output

outputs_Test = sim(net,input_Test);
outputs_Test=mapminmax('reverse',ooutputs_Test,ts);

errors = outputs_Test - Target_output;
plot(errors)

artificial neural ... , good tutorial, AI, Data Science, and Statistics , Statistics and Machine Le